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Domain model purity vs. domain model completeness (DDD Trilemma) I’ve been meaning to write this article for a long time and, finally, here it is: the topic of domain model purity versus domain model completeness. In this article, we’ll talk about a trilemma that comes up in each and every project. In fact, I received a dozen or so questions about this trilemma during the last year or two (slightl
March 29, 2023 Storing information in its highest form There’s an interesting guideline I’ve been meaning to write about for a long time. I call it Storing information in its highest form.
UPDATE 5/13/2021: the course has been published, check it now here: Validation and DDD. I’m working on a new Pluralsight course on the topic of validation and DDD, with the help of the FluentValidation library and .NET data annotations (attributes). So expect a couple of posts about validation in the near future. In the preparation for the course, I was re-reading some old articles about always-va
Validation and DDD can be a tricky combination. How to perform validation in a way that doesn’t lead to domain knowledge leakage? Recently, I came across an interesting discussion on the Jimmy Bogard’s blog. The post itself is quite old but the subject is still relevant. The discussion was about where to implement validation: in aggregates or in application services. It has resonated with me and I
In this post, we’ll make a deep dive into the DRY and DAMP principles and will talk about the false dichotomy around them. The DRY principle stands for "Don’t Repeat Yourself" and requires that any piece of domain knowledge has a single representation in your code base. In other words, in requires that you don’t duplicate the domain knowledge. The DAMP principle stands for "Descriptive and Meaning
The topic described in this article is a part of my Domain-Driven Design in Practice Pluralsight course. I wrote about entities and value objects some time ago. In this post, I’d like to talk about differences between Entity vs Value Object in more detail. I know, the topic isn’t new and there are a lot of articles on the Internet discussing it already. Nevertheless, I didn’t find any with an exha
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I believe you know what a value object is. Here’s a thorough article about it and what differentiates it from an Entity if you need a refresher: Entity vs Value Object: the ultimate list of differences. In short, value objects are the building blocks of your domain model. Ideally, you want any concept, however small it is, to be represented by a value object. Got a user with an email? Wrap this em
In this article, I’d like to clarify the differences in DTO vs Value Object vs POCO where DTO stands for Data Transfer Object, and POCO is Plain Old CLR Object, also known as POJO in Java environment. First of all, I want to make a note regarding Value Object. There’s a similar concept in C#, namely Value Type. It’s just an implementation detail of how objects are being stored in memory and I’m no
The topic described in this article is a part of my Database Delivery Best Practices Pluralsight course. Keeping track of your application’s database is not an easy task. Database schemas tend to mismatch in different environments, data in one of the databases may miss some crucial piece of data. Such occasions can be irritating, especially when caught in production. The situation gets worse when
CQRS is a pretty defined concept. Often, people say that you either follow CQRS or not, meaning that it is some kind of a binary choice. In this article, I’d like to show that there is some wriggle room in this notion and how different types of CQRS can look like. Type 0: no CQRS With this type, you don’t have any CQRS whatsoever. That means you have a domain model and you use your domain classes
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